Wen Xiao-le, Xu Han-qiu
Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, College of Environment and Resources, Fuzhou University, Fuzhou 350002, China.
Huan Jing Ke Xue. 2008 Sep;29(9):2441-7.
Three synchronal data collected on 2006-09-18 have been used in the study of the suspended solid concentration (SSC) of the lower Min River, which are in situ sampled water data, field-spectrometer measured spectral data and Landsat TM spectral data. Two models for predicting SSC have been proposed, one of which is based on field-spectrometer measured data and the other is on Landsat TM data. The statistical analysis of the field-spectroreter measured data has revealed that the reflectance of the SSC at the 690 nm has the strongest correlation with the in situ-sampled SSC data. The regression model can be expressed as SS = 116.2 (R690/R530) - 33.4. Furthermore, the model built upon the ratio of the reflectance at 690 nm to 530 nm has the best fitness with the in situ sampled SSC data. While the best predicting model for the Landsat TM data is achieved using the band combination of (TM2 + TM3)2 and is defined as SS = 3793.7 (R(TM3) + R(TM2)2 - 16.5. The assessment of the two models shows that the model on the field-spectrometer data has higher accuracy than that on the Landsat TM data but the difference is not big. This suggests that the Landsat TM data are still valuable in the prediction of the SSC if the field-spectrometer data are not available. Consequently, the predicting model based on the Landsat data has been applied in the study of the SSC of the lower Min River. The result shows that the model can efficiently reveal the SSC with its spatial distributional pattern features.
本研究使用了2006年9月18日采集的三个同步数据来研究闽江下游的悬浮固体浓度（SSC），这些数据分别是现场采样的水样数据、野外光谱仪测量的光谱数据和陆地卫星TM光谱数据。提出了两种预测SSC的模型，一种基于野外光谱仪测量数据，另一种基于陆地卫星TM数据。对野外光谱仪测量数据的统计分析表明，SSC在690nm处的反射率与现场采样的SSC数据相关性最强。回归模型可表示为SS = 116.2 (R690/R530) - 33.4。此外，基于690nm与530nm反射率之比建立的模型与现场采样的SSC数据拟合度最佳。而陆地卫星TM数据的最佳预测模型是使用(TM2 + TM3)2波段组合得到的，定义为SS = 3793.7 (R(TM3) + R(TM2)2 - 16.5。对这两种模型的评估表明，基于野外光谱仪数据的模型比基于陆地卫星TM数据的模型精度更高，但差异不大。这表明，如果没有野外光谱仪数据，陆地卫星TM数据在SSC预测中仍然具有价值。因此，基于陆地卫星数据的预测模型已应用于闽江下游SSC的研究。结果表明，该模型能够有效地揭示SSC及其空间分布模式特征。
